https://github.com/auralius/kalman-cpp

Kalman Filter, Extended Kalman Filter, and Unscented Kalman Filter implementation in C++
https://github.com/auralius/kalman-cpp

armadillo extended filter kalman second-order unscented-kalman-filter

Last synced: about 2 months ago
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Kalman Filter, Extended Kalman Filter, and Unscented Kalman Filter implementation in C++

• Host: GitHub
• URL: https://github.com/auralius/kalman-cpp
• Owner: auralius
• Created: 2017-01-14T20:48:57.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2023-02-15T03:47:06.000Z (over 1 year ago)
• Last Synced: 2024-02-06T06:41:44.643Z (5 months ago)
• Topics: armadillo, extended, filter, kalman, second-order, unscented-kalman-filter
• Language: C++
• Homepage: https://auralius.github.io/kalman-cpp/
• Size: 27 MB
• Stars: 27
• Watchers: 4
• Forks: 7
• Open Issues: 3
• Metadata Files:
  • Readme: README.md

Lists

• made-in-indonesia - kalman-cpp - `Kalman Filter, Extended Kalman Filter, and Unscented Kalman Filter implementation in C++` *by [Auralius Manurung](https://github.com/auralius)* (K)
• awesome-made-by-indonesian - kalman-cpp - `Kalman Filter, Extended Kalman Filter, and Unscented Kalman Filter implementation in C++` *by [Auralius Manurung](https://github.com/auralius)* (K)

README

        
# kalman-cpp

[![C/C++ CI](https://github.com/auralius/kalman-cpp/actions/workflows/c-cpp.yml/badge.svg)](https://github.com/auralius/kalman-cpp/actions/workflows/c-cpp.yml)

## Kalman filter and extended Kalman filter implementation in C++ 

Implemented filters so far: 

* Kalman filter  

* Extended Kalman filter  

* Second-order extended Kalman filter  

* Unscented Kalman filter  

Please use cmake to build all the codes.

The steps to compile are:

```

mkdir build

cd build

cmake ..

make

```  

## Windows System  

In a Windows system, a Visual Studio solution file (VS 2019) is provided. 

## Dependencies

This library utilizes [Armadillo](http://arma.sourceforge.net). 

In Windows system, the armadillo library is provided in "windows-libs" folder. 

**The contents of windows-libs.zip need to be first extracted.** 

Armadillo itself is very easy to use. 

More information on the Armadillo can be found [here](http://arma.sourceforge.net/docs.html).

## blas and lapack

By default, now kalman-cpp uses blas and lapack. For Windows machine, working with blas and lapack is a messy stuff. Thus, we will use the precompiled blas and lapack from:  https://www.fi.muni.cz/~xsvobod2/misc/lapack/. 

The precompiled blas and lapack libraries are included in **windows-libs.zip**. There are four LIB files. Additionally, in "bin" folder, there are four corresponding DLL files as well. There are four files because two files are for the 32-bit platform, and the other two files are for the 64-bit platform. 

**The compiled binary must always be located in the same folder as these DLL files.**

## MATLAB m-files for plotting

MATLAB m-files for each example are provided in 'm-files' folder. Octave can also be used instead of MATLAB.

## Documentation

  

https://auralius.github.io/kalman-cpp/